arXiv subjects

Qing Huang

Publications and source records attributed to Qing Huang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Evaluation of tetracycline photocatalytic degradation using NiFe2O4/CeO2/GO nanocomposite for environmental remediation: In silico molecular docking, Antibacterial performance, degradation pathways, and DFT calculations

Graphene-based nanostructures with distinct structural and physicochemical characteristics may be able to photodegrade antibiotics effectively. Herein, this study reports the successful synthesis of NiFe2O4/CeO2/GO nanocomposite (NC) by anchoring NiFe2O4/CeO2 to the surface of GO (Graphene oxide). All state-of-the-art characterization techniques investigated the nanostructure, crystallinity, phonon modes, chemical composition analysis, elemental composition, surface area, magnetic properties, and optical band gap. Hydrothermal approach assisted NiFe2O4/CeO2/GO catalyst showed better charge carrier separation and prompted the tetracycline (TC-HCl) photocatalytic degradation under visible light. Following 90 minutes of exposure to visible light, NiFe2O4/CeO2/GO nanocomposite demonstrated superior photocatalytic activity, with a TC-HCl degradation rate of 95%. Reasonable mechanisms of tetracycline degradation were proposed where the OH and O played a leading role based on identified intermediates. Moreover, tetracycline photodegradation intermediates and the optimal pathway were identified using LC-MS spectrometry. This study also performed Density Functional Theory (DFT) calculations for the prepared materials to validate the experimental data. In vitro, antibacterial studies were consistent with the molecular docking investigations of the NiFe2O4/CeO2/GO nanocomposite against DNA gyrase and FabI from Escherichia coli (E. coli) and Staphylococcus aureus (S.aureus). Lastly, the outcomes revealed a new potential for NiFe2O4/CeO2/GO nanocomposite for improved photocatalytic performance, making it a promising photocatalyst for wastewater treatment.

cond-mat.mtrl-sci↗

arXiv2025

Using Label-Free Raman Spectroscopy Integrated with Microfluidic Chips to Probe Ferroptosis Networks in Cells

Ferroptosis, a regulated form of cell death driven by oxidative stress and lipid peroxidation, has emerged as a pivotal research focus with implications across various cellular contexts. In this study, we employed a multifaceted approach, integrating label-free Raman spectroscopy and microfluidics to study the mechanisms underpinning ferroptosis. Our investigations included the ferroptosis initiation based on the changes in the lipid Raman band at 1436 cm-1 under different cellular states, the generation of reactive oxygen species (ROS), lipid peroxidation, DNA damage/repair, and mitochondrial dysfunction. Importantly, our work highlighted the dynamic role of vital cellular components, such as NADPH, ferredoxin clusters, and key genes like GPX-4, VDAC2, and NRF2, as they collectively influenced cellular responses to redox imbalance and oxidative stress. Quantum mechanical (QM) and molecular docking simulations (MD) provided further evidence of interactions between the ferredoxin (containing 4Fe-4S clusters), NADPH and ROS which led to the production of reactive Fe species in the cells. As such, our approach offered a real-time, multidimensional perspective on ferroptosis, surpassing traditional biological methods, and providing valuable insights for therapeutic interventions in diverse biomedical contexts.

physics.bio-ph↗

arXiv2025

Explore-Construct-Filter: An Automated Framework for Rich and Reliable API Knowledge Graph Construction

The API Knowledge Graph (API KG) is a structured network that models API entities and their relations, providing essential semantic insights for tasks such as API recommendation, code generation, and API misuse detection. However, constructing a knowledge-rich and reliable API KG presents several challenges. Existing schema-based methods rely heavily on manual annotations to design KG schemas, leading to excessive manual overhead. On the other hand, schema-free methods, due to the lack of schema guidance, are prone to introducing noise, reducing the KG's reliability. To address these issues, we propose the Explore-Construct-Filter framework, an automated approach for API KG construction based on large language models (LLMs). This framework consists of three key modules: 1) KG exploration: LLMs simulate the workflow of annotators to automatically design a schema with comprehensive type triples, minimizing human intervention; 2) KG construction: Guided by the schema, LLMs extract instance triples to construct a rich yet unreliable API KG; 3) KG filtering: Removing invalid type triples and suspicious instance triples to construct a rich and reliable API KG. Experimental results demonstrate that our method surpasses the state-of-the-art method, achieving a 25.2% improvement in F1 score. Moreover, the Explore-Construct-Filter framework proves effective, with the KG exploration module increasing KG richness by 133.6% and the KG filtering module improving reliability by 26.6%. Finally, cross-model experiments confirm the generalizability of our framework.

cs.SE↗

arXiv2025

Organometallic-Inorganic Hybrid MXenes with Tunable Superconductivity

Ti-based two-dimensional transition-metal carbides (MXenes) have attracted attention due to their superior properties and are being explored across various applications1,2. Despite their versatile properties, superconductivity has never been demonstrated, not even predicted, for this important group of 2D materials. In this work, we have introduced an electrochemical intercalation protocol to construct versatile organometallic-inorganic hybrid MXenes and achieved tunable superconductivity in the metallocene-modified layered crystals. Through structural editing of MXene matrix at atomic scale and meticulously modulated intercalation route, Ti3C2Tx intercalated with metallocene species exhibits a superconductive transition temperature (Tc) of 10.2 K. Guest intercalation induced electron filling and strain engineering are responsible for the emerging superconductivity in this intrinsically non-superconducting material. Theoretically, simulated electron-phonon interaction effects further elucidate the nature of the changes in Tc. Furthermore, the Tc of crafted artificial superlattices beyond Ti-based MXenes have been predicted, offering a general strategy for engineering superconductivity and magnetism in layered hybrid materials.

cond-mat.supr-con↗

arXiv2024

Sublayers Editing of Covalent MAX Phase for Nanolaminated Early Transition Metal Compounds

Two-dimensional transition metal carbides and nitrides (MXenes) have gained popularity in fields such as energy storage, catalysis, and electromagnetic interference due to their diverse elemental compositions and variable surface terminations (T). Generally, the synthesis of MXene materials involves etching the weak M-A metallic bonds in the ternary layered transition metal carbides and nitrides (MAX phase) using HF acid or Lewis acid molten salts, while the strong M-X covalent bonds preserve the two-dimensional framework structure of MXenes. On the other hand, the MAX phase material family also includes a significant class of members where the A site is occupied by non-metal main group elements (such as sulfur and phosphorus), in which both M-A and M-X are covalent bond-type sublayers. The aforementioned etching methods cannot be used to synthesize MXene materials from these parent phases. In this work, we discovered that the covalent bond-type M-A and M-X sublayers exhibit different reactivity with some inorganic materials in a high-temperature molten state. By utilizing this difference in reactivity, we can structurally modify these covalent sublayers, allowing for the substitution of elements at the X site (from B to Se, S, P, C) and converting non-metal A site atoms in non-van der Waals (non-vdW) MAX phases into surface atoms in vdW layered materials. This results in a family of early transition metal Xide chalcogenides (TMXCs) that exhibit lattice characteristics of both MXenes and transition metal chalcogenides. Using electron-donor chemical scissors, these TMXC layered materials can be further exfoliated into monolayer nanosheets. The atomic configurations of each atom in these monolayer TMXCs are the same as those of conventional MXenes, but the oxidation states of the M-site atoms can be regulated by both X-site atoms and intercalated cations.

cond-mat.mtrl-sci↗

arXiv2024

DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation

Accurately segmenting lesions in ultrasound images is challenging due to the difficulty in distinguishing boundaries between lesions and surrounding tissues. While deep learning has improved segmentation accuracy, there is limited focus on boundary quality and its relationship with body structures. To address this, we introduce UBBS-Net, a dual-branch deep neural network that learns the relationship between body and boundary for improved segmentation. We also propose a feature fusion module to integrate body and boundary information. Evaluated on three public datasets, UBBS-Net outperforms existing methods, achieving Dice Similarity Coefficients of 81.05% for breast cancer, 76.41% for brachial plexus nerves, and 87.75% for infantile hemangioma segmentation. Our results demonstrate the effectiveness of UBBS-Net for ultrasound image segmentation. The code is available at https://github.com/apple1986/DBF-Net.

eess.IV↗

arXiv2024

Anomalous Proximitized Transport in Metal/Quantum Magnet Heterostructure $\rm{Bi_{2}Ir_{2}O_{7}/Yb_{2}Ti_{2}O_{7}}$

Fluctuations of quantum spins play a crucial role in the emergence of exotic magnetic phases and excitations. The lack of the charge degree of freedom in insulating quantum magnets, however, precludes such fluctuations from mediating electronic transport. Here we show that the quantum fluctuations of a localized frustrated magnet induce strong proximitized charge transport of the conduction electrons in a synthetic heterostructure comprising an epitaxial $\rm{Bi_{2}Ir_{2}O_{7}}$ ultrathin film on the single crystal of $\rm{Yb_{2}Ti_{2}O_{7}}$. The proximity effects are evidenced by the scaling behavior of the $\rm{Bi_{2}Ir_{2}O_{7}}$ resistance in correspondance with the dynamic scaling of the dynamic spin correlation function of $\rm{Yb_{2}Ti_{2}O_{7}}$, which is a result of quantum fluctuations near a multi-phase quantum critical point. The proximitized transport in $\rm{Bi_{2}Ir_{2}O_{7}}$ can be effectively tuned by magnetic field through suppressing the quantum spin fluctuations as well as inducing transitions via magnetic anisotropy in $\rm{Yb_{2}Ti_{2}O_{7}}$. Our work establishes a new pathway for harnessing quantum spin fluctuations in magnetic insulators with electric transport, offering exciting prospects for potential applications in the realm of quantum spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Field tunable BKT and quantum phase transitions in spin-1/2 triangular lattice antiferromagnet

Quantum magnetism is one of the most active fields for exploring exotic phases and phase transitions. The recently synthesized Na2BaCo(PO4)2 (NBCP) is an ideal material incarnation of the spin-1/2 easy axis triangular lattice antiferromagnet (TLAF). Experimental evidence shows that NBCP hosts the spin supersolid state with a giant magnetocaloric effect. It was also proposed that the applied magnetic field B can drive the system through Berezinskii-Kosterlitz-Thouless (BKT) and other richer quantum phase transitions. However, the detection of these transitions is challenging because they onset at extremely low temperature T at around 60 mK, and the measurement of the magnetic susceptibility of these transitions requires high sensitivity. With the help of our newly developed gradient force magnetometer in a dilution refrigerator, we constructed the contour diagram of the magnetic susceptibility in the B-T phase diagram in T as cold as 30 mK. These results provide a more comprehensive and accurate understanding of the several field-tunable quantum phase transitions and BKT melting of the spin supersolidity, which are especially significant when their giant magnetocaloric effects highlight potential applications for sub-Kelvin refrigeration under concerns about global helium shortages.

cond-mat.str-el↗

arXiv2024

A^3-CodGen: A Repository-Level Code Generation Framework for Code Reuse with Local-Aware, Global-Aware, and Third-Party-Library-Aware

LLM-based code generation tools are essential to help developers in the software development process. Existing tools often disconnect with the working context, i.e., the code repository, causing the generated code to be not similar to human developers. In this paper, we propose a novel code generation framework, dubbed A^3-CodGen, to harness information within the code repository to generate code with fewer potential logical errors, code redundancy, and library-induced compatibility issues. We identify three types of representative information for the code repository: local-aware information from the current code file, global-aware information from other code files, and third-party-library information. Results demonstrate that by adopting the A^3-CodGen framework, we successfully extract, fuse, and feed code repository information into the LLM, generating more accurate, efficient, and highly reusable code. The effectiveness of our framework is further underscored by generating code with a higher reuse rate, compared to human developers. This research contributes significantly to the field of code generation, providing developers with a more powerful tool to address the evolving demands in software development in practice.

cs.SE↗

arXiv2024

Semiconductive and Ferromagnetic Lanthanide MXenes Derived from Carbon Intercalated Two-dimensional Halides

Two-dimensional (2D) magnetic semiconductors are a key focus in developing next-generation information storage technologies. MXenes, as emerging 2D early transition metal carbides and nitrides, offer versatile compositions and tunable chemical structures. Incorporating lanthanide metals, with their unique role of 4f-electrons in engineering physical properties, into MXenes holds potential for advancing technological applications. However, the scarcity of lanthanide-containing ternary MAX phase precursors and the propensity of lanthanides to oxidize pose significant challenges to obtain lanthanide MXenes (Ln2CT2) via the top-down etching method. Here, we propose a general bottom-up methodology for lanthanide MXenes, that derive from carbon intercalated van der Waals building blocks of 2D halides. Compared to conventional MXenes conductors, the synthesized Ln2CT2 exhibit tunable band gaps spanning 0.32 eV to 1.22 eV that cover typical semiconductors such as Si (1.12 eV) and Ge (0.67 eV). Additionally, the presence of unpaired f-electrons endows Ln2CT2 with intrinsic ferromagnetism, with Curie temperatures ranging between 36 K and 60 K. Theoretical calculations reveal that, in contrast to traditional MXenes, the number of d-electrons states around the Fermi level are largely diminishes in bare Ln2C MXenes, and the halogen terminals can further exhaust these electrons to open band gaps. Meanwhile, the Ln-4f electrons in Ln2CT2 are highly localized and stay away from the Fermi level, contributing to the spin splitting for the observed ferromagnetic behavior. Lanthanide MXenes hold immense promise for revolutionizing future applications in spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Gaseous Scissor-mediated Electrochemical Exfoliation of Halogenated MXenes and its Boosting in Wear-Resisting Tribovoltaic Devices

Two-dimensional transition metal carbides (MXenes), especially their few-layered nanosheets, have triggered burgeoning research attentions owing to their superiorities including extraordinary conductivity, accessible active surface, and adjustable processability. Molten salts etching route further achieves their controllable surface chemistry. However, the method encounters challenges in achieving few-layer structures due to more complex delamination behaviors. Herein, we present an efficient strategy to fabricate Cl- or Br-terminated MXene nanoflakes with few-layers, achieved by electrochemical intercalation of Li ions and concomitant solvent molecules in the electrolyte solution, with gaseous scissors (propylene molecules) to break up interlayer forces. By controlling cut-off voltages, the optimal protocol results in nanosheets with an ultrahigh yield (~93%) and preserved surface chemistry. The resultant MXenes dispersions were employed as lubricants to enhance tribovoltaic nanogenerators, where Ti3C2Br2 displayed superior electrical output. These findings facilitate the understanding of MXenes' intrinsic physical properties and enable the nanoengineering of advanced electronic devices.

physics.app-ph↗

arXiv2024

Evidence for field induced quantum spin liquid behavior in a spin-1/2 honeycomb magnet

One of the most important issues in modern condensed matter physics is the realization of fractionalized excitations, such as the Majorana excitations in the Kitaev quantum spin liquid. To this aim, the 3d-based Kitaev material Na2Co2TeO6 is a promising candidate whose magnetic phase diagram of B // a* contains a field-induced intermediate magnetically disordered phase within 7.5 T < |B| < 10 T. The experimental observations, including the restoration of the crystalline point group symmetry in the angle-dependent torque and the coexisting magnon excitations and spinon-continuum in the inelastic neutron scattering spectrum, provide strong evidence that this disordered phase is a field induced quantum spin liquid with partially polarized spins. Our variational Monte Carlo simulation with the effective K-J1-Γ-Γ'-J3 model reproduces the experimental data and further supports this conclusion.

cond-mat.str-el↗

arXiv2024

Magnetic properties of RE2O2CO3 (RE = Pr, Nd, Gd, Tb, Dy, Ho, Er, Yb) with a rare earth-bilayer of triangular lattice

Polycrystalline samples of RE2O2CO3 (RE = Pr, Nd, Gd, Tb, Dy, Ho, Er, and Yb) with a unique rare-earth bilayer of triangular lattice were synthesized and studied by DC and AC magnetic susceptibility. Data reveals various magnetic ground states including (i) a nonmagnetic ground state for the Pr sample; (ii) long range magnetic ordering for the Nd, Gd, Tb, Dy, Ho, and Er samples. Besides the Gd sample, they exhibit field-induced spin state transitions. More interestingly, the series spin state transitions in the Nd and Dy samples could be attributed to the field-induced up-up-down (UUD) spin structure. Neutron powder diffraction (NPD) measurements of the Er sample suggest a spiral spin structure below its TN; and (iii) a short-range ordering for the Yb sample. The disrupted inter-layer interaction due to the shift of Yb3+ ions within the bilayer prevents long range magnetic ordering down to 30 mK and makes it another Yb-related triangular lattice antiferromagnet that has the potential to realize a quantum spin liquid state.

cond-mat.str-el↗

arXiv2024

Out-of-plane magnetic phase diagram of Kitaev quantum spin liquid candidate Na2Co2TeO6

We have investigated the magnetic properties and mapped out the phase diagram of the honeycomb magnet Na2Co2TeO6 with Co 3d7 in out-of-plane magnetic fields. This material has previously been proposed to show nearest-neighbor Kitaev interactions between Co spins and maybe even Kitaev quantum spin liquid behavior in high fields. At low magnetic fields, we observe a thermal phase transition at TN = 27 K, transitioning from a paramagnetic state to a canonical ferrimagnetic state. Under the application of magnetic fields, a spin flop-like phase transition occurred before saturation of J = 1/2 between 10 K and TN. Below 10 K, a peak-dip-peak structure emerges between 10 and 17 T in the magnetic susceptibility (dM/dH) before the magnetic saturation, reminiscent of magnetic plateau behavior. The measurement of the magnetocaloric effect also shows dip-peak-dip behavior in this field range. Our data can be explained by an XXZ model with a single ion anisotropy and possibly small Kitaev and Γ exchange interactions. We also unambiguously determined the magnetization saturation field that helps constrain the energy scale of the exchange interactions

cond-mat.str-el↗

arXiv2023

Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains

The emergence of foundation models, such as large language models (LLMs) GPT-4 and text-to-image models DALL-E, has opened up numerous possibilities across various domains. People can now use natural language (i.e. prompts) to communicate with AI to perform tasks. While people can use foundation models through chatbots (e.g., ChatGPT), chat, regardless of the capabilities of the underlying models, is not a production tool for building reusable AI services. APIs like LangChain allow for LLM-based application development but require substantial programming knowledge, thus posing a barrier. To mitigate this, we propose the concept of AI chain and introduce the best principles and practices that have been accumulated in software engineering for decades into AI chain engineering, to systematise AI chain engineering methodology. We also develop a no-code integrated development environment, Prompt Sapper, which embodies these AI chain engineering principles and patterns naturally in the process of building AI chains, thereby improving the performance and quality of AI chains. With Prompt Sapper, AI chain engineers can compose prompt-based AI services on top of foundation models through chat-based requirement analysis and visual programming. Our user study evaluated and demonstrated the efficiency and correctness of Prompt Sapper.

cs.SE↗

arXiv2023

Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus and affected by the characteristics of the input text.To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. This approach leverages the entire Web used to pre-train LLMs as a knowledge base and is insensitive to the context and complexity of input texts. To ensure accurate inference, we design our analytic flow as an AI Chain with three AI modules: API FQN Parser, API Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN parser and API Relation Decider module are 0.81 and 0.83, respectively. Using the generative capacity of the LLM and our approach's inference capability, we achieve an average F1 value of 0.76 under the three datasets, significantly higher than the state-of-the-art method's average F1 value of 0.40. Compared to CoT-based method, our AI Chain design improves the inference reliability by 67%, and the AI-crowd-intelligence strategy enhances the robustness of our approach by 26%.

cs.SE↗

arXiv2023

Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain

API recommendation methods have evolved from literal and semantic keyword matching to query expansion and query clarification. The latest query clarification method is knowledge graph (KG)-based, but limitations include out-of-vocabulary (OOV) failures and rigid question templates. To address these limitations, we propose a novel knowledge-guided query clarification approach for API recommendation that leverages a large language model (LLM) guided by KG. We utilize the LLM as a neural knowledge base to overcome OOV failures, generating fluent and appropriate clarification questions and options. We also leverage the structured API knowledge and entity relationships stored in the KG to filter out noise, and transfer the optimal clarification path from KG to the LLM, increasing the efficiency of the clarification process. Our approach is designed as an AI chain that consists of five steps, each handled by a separate LLM call, to improve accuracy, efficiency, and fluency for query clarification in API recommendation. We verify the usefulness of each unit in our AI chain, which all received high scores close to a perfect 5. When compared to the baselines, our approach shows a significant improvement in MRR, with a maximum increase of 63.9% higher when the query statement is covered in KG and 37.2% when it is not. Ablation experiments reveal that the guidance of knowledge in the KG and the knowledge-guided pathfinding strategy are crucial for our approach's performance, resulting in a 19.0% and 22.2% increase in MAP, respectively. Our approach demonstrates a way to bridge the gap between KG and LLM, effectively compensating for the strengths and weaknesses of both.

cs.SE↗

arXiv2023

Static and dynamical properties of the spin-5/2 nearly ideal triangular lattice antiferromagnet Ba3MnSb2O9

We study the ground state and spin excitations in Ba3MnSb2O9, an easy-plane S = 5/2 triangular lattice antiferromagnet. By combining single-crystal neutron scattering, electric spin resonance (ESR), and spin wave calculations, we determine the frustrated quasi-two-dimensional spin Hamiltonian parameters describing the material. While the material has a slight monoclinic structural distortion, which could allow for isosceles-triangular exchanges and biaxial anisotropy by symmetry, we observe no deviation from the behavior expected for spin waves in the in-plane 120o state. Even the easy-plane anisotropy is so small that it can only be detected by ESR in our study. In conjunction with the quasi-two-dimensionality, our study establishes that Ba3MnSb2O9 is a nearly ideal triangular lattice antiferromagnet with the quasi-classical spin S = 5/2, which suggests that it has the potential for an experimental study of Z- or Z2-vortex excitations.

cond-mat.str-el↗